T cells are central players in the fight against cancer, capable of recognizing and destroying tumor cells. However, tumors often find ways to evade this immune response, creating challenges for effective treatment. In this review, we explore how different T cell subsets—including cytotoxic T cells, helper T cells, regulatory T cells, and unconventional T cells—contribute to tumor progression or suppression. We also delve into key mechanisms, such as immune checkpoints and metabolic pathways, that shape T cell behavior in the tumor microenvironment. Advances in cancer immunotherapy, including immune checkpoint inhibitors (ICIs), T cell engagers (TCEs), adoptive T cell therapies (ACTs), chimeric antigen receptor (CAR) T cell therapies, and cancer vaccines, have transformed cancer treatment and provided new hope for patients. However, challenges such as treatment resistance, limited efficacy in solid tumors, and therapy-associated toxicities remain significant barriers to broader clinical success. We discuss innovative strategies to tackle these challenges, including combination therapies and next-generation T cell engineering approaches. By connecting the biology of T cells with cutting-edge therapeutic advances, this review aims to inspire progress in the development of more effective and personalized cancer treatments.

For more than a decade, extracellular vesicles (EVs) have been in focus of science. Once thought to be an efficient way to eliminate undesirable cell content, EVs are now well-accepted as being an important alternative to cytokines and chemokines in cell-to-cell communication route. With their cargos, mainly consisting of functional proteins, lipids and nucleic acids, they can activate signalling cascades and thus change the phenotype of recipient cells at local and systemic levels. Their substantial role as modulators of various physiological and pathological processes is acknowledged. Importantly, more and more evidence arises that EVs play a pivotal role in many stages of carcinogenesis. Via EV-mediated communication, tumour cells can manipulate cells from host immune system or from the tumour microenvironment, and, ultimately, they promote tumour progression and modulate host immunity towards tumour’s favour. Additionally, the role of EVs in modulating resistance to pharmacological and radiological therapy of many cancer types has become evident lately. Our understanding of EV biology and their role in cancer promotion and drug resistance has evolved considerably in recent years. In this review, we specifically discuss the current knowledge on the association between EVs and gastrointestinal (GI) and liver cancers, including their potential for diagnosis and treatment.

Dendritic cells (DCs) are major antigen-presenting cells that connect innate and adaptive immunity. Hepatic DCs are less activated and contribute to maintain the tolerogenic environment of the liver under steady state. Several studies indicated DCs in metabolic dysfunction-associated steatohepatitis (MASH), representing a substantial burden on healthcare systems due to its association with liver-related morbidity and mortality. Studies highlighted the potential disease-promoting role of liver DCs in the development of MASH while other experimental systems suggested their protective role. This review discusses this controversy and the current understanding of how DCs affect the pathogenesis of MASH.

Cholangiocarcinoma is a rare and aggressive cancer type with limited therapeutic options. Several novel antibody–drug conjugates (ADC) have demonstrated promising antitumor activity in solid tumors. This study aimed to investigate the expression and the potential prognostic role of the protein targets of the recently developed ADCs in cholangiocarcinoma. Moreover, the study aimed to establish patient-derived tumor organoids (PDO) and to employ them for in vitro ADC testing. We evaluated the expression of TROP2, NECTIN4, folate receptor 1, HER2, and HER3 via IHC in a cholangiocarcinoma tissue microarray (n = 113) and analyzed the expression level with respect to clinicopathologic parameters. Furthermore, we generated cholangiocarcinoma PDO culture lines and used them to test the antitumor activity of ADCs in vitro. IHC analyses revealed that TROP2 was the most frequently expressed (91% of cases), followed by folate receptor 1 (51%), NECTIN4 (49%), HER3 (20%), and HER2 (7%). TROP2 showed moderate to high expression (H-score ≥100) in 74% of cases. No significant correlations with overall or disease-free survival, tumor grade, or tumor stage were observed. Six cholangiocarcinoma PDO lines were successfully established (55% success rate). All PDO lines expressed TROP2 concordantly with their parental tumors and showed growth inhibition (IC50 = 0.1–0.4 μg/mL) in response to sacituzumab govitecan (TROP2-targeting ADC). This study reveals that TROP2 is widely expressed in cholangiocarcinoma. Moreover, it provides preclinical evidence for the potential of the use of sacituzumab govitecan as a novel therapeutic strategy in treating patients with cholangiocarcinoma.

In our 2017 publication, we introduced a novel concept for liver cancer detection using large extracellular vesicles (EVs), specifically AnnV⁺EpCAM⁺ASGPR1⁺ tumor-associated microparticles. Despite promising biology, the diagnostic utility was limited by the analytical tools available at the time, resulting in modest performance (AUC 0.70, 75% sensitivity, 47% specificity). In the present study, we revisited this legacy dataset – now supplemented with unpublished but previously collected measurements (N = 166) – and applied modern artificial intelligence-assisted analytical strategies. We evaluated a wide range of combinatorial models using Random Forest and Decision Tree classifiers, incorporating both rare large EV populations and classical serological markers (AFP, CEA, CA19-9, bilirubin). The RF model combining AnnV⁺EpCAM⁺CD133⁺gp38⁺ large EVs with classical serological markers achieved a mean accuracy of 88.2%, recall of 91.6%, and F1-score of 87.0% across 10 stratified train-test runs. This substantially outperformed earlier analysis efforts. To support clinical translation, we additionally developed a simplified decision tree model based on the same marker inputs, offering a visual and rule-based alternative that remained robust, with an average accuracy of 86.6% and recall of 87.3% and a sensitivity of 94% and a specificity of 78% (70/30 split) across 10 stratified train-test runs. This study demonstrates how legacy data, when re-analyzed with artificial intelligence-supported tools, can reveal clinically actionable insight. Large EVs – particularly derived from rare progenitor-like subpopulations – combined with classical serum markers, provide a promising non-invasive screening approach. This study revisits a legacy extracellular vesicle dataset using modern artificial intelligence-assisted analysis to identify synergistic biomarker combinations for liver cancer screening. The results demonstrate that archived data, when re-analyzed with advanced computational tools, can yield novel and clinically relevant insights – particularly for screening liver malignancies. This approach may serve as a reproducible and transparent blueprint for similar efforts in liver diseases, oncology, and biomedical research more broadly. These findings are relevant to clinicians, translational researchers, and policymakers aiming to advance precision screening and early detection strategies. More broadly, this work underscores the role of artificial intelligence as a scientific collaborator and offers a model for responsible, sustainable innovation in biomedical research.